from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
Daal4py_kmeans_short: 0h 0m 1s
Daal4py_ridge: 0h 0m 1s
Kmeans_short: 0h 0m 2s
Daal4py_logisticregression: 0h 0m 3s
Daal4py_kmeans_tall: 0h 0m 7s
Ridge: 0h 0m 10s
Logisticregression: 0h 0m 18s
Kmeans_tall: 0h 0m 20s
Daal4py_kneighborsclassifier_kd_tree: 0h 0m 24s
Daal4py_kneighborsclassifier: 0h 2m 20s
Kneighborsclassifier_kd_tree: 0h 2m 20s
Catboost: 0h 5m 1s
Xgboost: 0h 5m 2s
Lightgbm: 0h 5m 5s
Histgradientboostingclassifier: 0h 5m 15s
Catboost_symmetric: 0h 5m 22s
Kneighborsclassifier: 0h 40m 14s
Total: 1h 12m 13s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config_file_path="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.134 | 0.000 | 5.953 | 0.000 | 1 | 1 | NaN | NaN | 0.463 | 0.000 | 0.290 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 11.880 | 0.083 | 0.000 | 0.012 | 1 | 1 | 0.687 | 0.831 | 1.656 | 0.008 | 7.173 | 0.060 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.174 | 0.000 | 0.000 | 0.174 | 1 | 1 | 1.000 | 1.000 | 0.077 | 0.000 | 2.263 | 0.012 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.126 | 0.000 | 6.371 | 0.000 | -1 | 100 | NaN | NaN | 0.449 | 0.000 | 0.280 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 32.708 | 0.000 | 0.000 | 0.033 | -1 | 100 | 0.950 | 0.936 | 1.715 | 0.005 | 19.077 | 0.056 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.163 | 0.012 | 0.000 | 0.163 | -1 | 100 | 1.000 | 1.000 | 0.077 | 0.000 | 2.124 | 0.151 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.123 | 0.000 | 6.480 | 0.000 | -1 | 5 | NaN | NaN | 0.448 | 0.000 | 0.275 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 32.488 | 0.000 | 0.000 | 0.032 | -1 | 5 | 0.822 | 0.936 | 1.716 | 0.006 | 18.932 | 0.063 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.162 | 0.015 | 0.000 | 0.162 | -1 | 5 | 1.000 | 1.000 | 0.077 | 0.000 | 2.096 | 0.189 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.121 | 0.000 | 6.593 | 0.000 | -1 | 1 | NaN | NaN | 0.450 | 0.000 | 0.270 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 22.241 | 0.104 | 0.000 | 0.022 | -1 | 1 | 0.687 | 0.831 | 1.655 | 0.005 | 13.442 | 0.075 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.155 | 0.017 | 0.000 | 0.155 | -1 | 1 | 1.000 | 1.000 | 0.076 | 0.000 | 2.026 | 0.223 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.119 | 0.000 | 6.740 | 0.000 | 1 | 100 | NaN | NaN | 0.449 | 0.000 | 0.265 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 22.094 | 0.020 | 0.000 | 0.022 | 1 | 100 | 0.950 | 0.713 | 1.654 | 0.007 | 13.355 | 0.061 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.185 | 0.001 | 0.000 | 0.185 | 1 | 100 | 1.000 | 0.000 | 0.077 | 0.001 | 2.392 | 0.034 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.119 | 0.000 | 6.746 | 0.000 | 1 | 5 | NaN | NaN | 0.449 | 0.000 | 0.264 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 22.136 | 0.031 | 0.000 | 0.022 | 1 | 5 | 0.822 | 0.713 | 1.658 | 0.014 | 13.353 | 0.116 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.183 | 0.001 | 0.000 | 0.183 | 1 | 5 | 1.000 | 0.000 | 0.079 | 0.008 | 2.315 | 0.226 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.053 | 0.000 | 0.304 | 0.000 | 1 | 1 | NaN | NaN | 0.095 | 0.000 | 0.554 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 9.395 | 0.021 | 0.000 | 0.009 | 1 | 1 | 0.988 | 0.986 | 0.254 | 0.000 | 36.922 | 0.097 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.013 | 0.000 | 0.000 | 0.013 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 2.611 | 0.154 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.309 | 0.000 | -1 | 100 | NaN | NaN | 0.095 | 0.000 | 0.547 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 29.418 | 0.095 | 0.000 | 0.029 | -1 | 100 | 0.989 | 0.986 | 0.302 | 0.002 | 97.425 | 0.727 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.024 | 0.002 | 0.000 | 0.024 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 4.656 | 0.490 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.310 | 0.000 | -1 | 5 | NaN | NaN | 0.095 | 0.000 | 0.545 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 29.550 | 0.119 | 0.000 | 0.030 | -1 | 5 | 0.991 | 0.986 | 0.302 | 0.002 | 97.876 | 0.785 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.023 | 0.001 | 0.000 | 0.023 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 4.562 | 0.354 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.306 | 0.000 | -1 | 1 | NaN | NaN | 0.095 | 0.000 | 0.550 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.764 | 0.072 | 0.000 | 0.020 | -1 | 1 | 0.988 | 0.986 | 0.255 | 0.001 | 77.452 | 0.326 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.017 | 0.002 | 0.000 | 0.017 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 3.516 | 0.533 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.308 | 0.000 | 1 | 100 | NaN | NaN | 0.095 | 0.000 | 0.546 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.040 | 0.008 | 0.000 | 0.019 | 1 | 100 | 0.989 | 0.980 | 0.254 | 0.002 | 74.986 | 0.563 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.000 | 0.000 | 0.020 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 3.859 | 0.230 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.053 | 0.000 | 0.303 | 0.000 | 1 | 5 | NaN | NaN | 0.095 | 0.000 | 0.559 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.012 | 0.013 | 0.000 | 0.019 | 1 | 5 | 0.991 | 0.980 | 0.253 | 0.000 | 75.088 | 0.122 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.019 | 0.001 | 0.000 | 0.019 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 3.703 | 0.297 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.640 | 0.000 | 0.030 | 0.000 | -1 | 5 | NaN | NaN | 0.657 | 0.000 | 4.016 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.780 | 0.003 | 0.000 | 0.001 | -1 | 5 | 0.975 | 0.978 | 0.163 | 0.002 | 4.794 | 0.073 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 10.100 | 4.876 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.665 | 0.000 | 0.030 | 0.000 | 1 | 1 | NaN | NaN | 0.634 | 0.000 | 4.207 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.674 | 0.004 | 0.000 | 0.001 | 1 | 1 | 0.948 | 0.978 | 0.162 | 0.001 | 4.163 | 0.035 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 3.320 | 1.637 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.636 | 0.000 | 0.030 | 0.000 | -1 | 1 | NaN | NaN | 0.626 | 0.000 | 4.212 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.417 | 0.004 | 0.000 | 0.000 | -1 | 1 | 0.948 | 0.959 | 0.090 | 0.001 | 4.658 | 0.059 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 11.737 | 5.854 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.625 | 0.000 | 0.030 | 0.000 | 1 | 100 | NaN | NaN | 0.624 | 0.000 | 4.204 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.151 | 0.029 | 0.000 | 0.004 | 1 | 100 | 0.975 | 0.978 | 0.487 | 0.003 | 8.531 | 0.076 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 1.000 | 1.000 | 0.001 | 0.000 | 5.219 | 2.321 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.688 | 0.000 | 0.030 | 0.000 | -1 | 100 | NaN | NaN | 0.654 | 0.000 | 4.111 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.288 | 0.007 | 0.000 | 0.002 | -1 | 100 | 0.975 | 0.959 | 0.090 | 0.001 | 25.555 | 0.248 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 23.019 | 11.592 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.620 | 0.000 | 0.031 | 0.000 | 1 | 5 | NaN | NaN | 0.626 | 0.000 | 4.186 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.279 | 0.011 | 0.000 | 0.001 | 1 | 5 | 0.975 | 0.978 | 0.489 | 0.009 | 2.615 | 0.054 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 1.712 | 0.740 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.739 | 0.000 | 0.022 | 0.000 | -1 | 5 | NaN | NaN | 0.416 | 0.000 | 1.775 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.025 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.979 | 0.984 | 0.001 | 0.000 | 23.965 | 6.157 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 17.618 | 13.194 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.736 | 0.000 | 0.022 | 0.000 | 1 | 1 | NaN | NaN | 0.416 | 0.000 | 1.770 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.022 | 0.001 | 0.001 | 0.000 | 1 | 1 | 0.971 | 0.984 | 0.001 | 0.000 | 20.604 | 5.305 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.660 | 4.456 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.728 | 0.000 | 0.022 | 0.000 | -1 | 1 | NaN | NaN | 0.415 | 0.000 | 1.754 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.002 | 0.001 | 0.000 | -1 | 1 | 0.971 | 0.975 | 0.001 | 0.000 | 33.707 | 10.994 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 19.668 | 14.233 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.735 | 0.000 | 0.022 | 0.000 | 1 | 100 | NaN | NaN | 0.443 | 0.000 | 1.657 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.045 | 0.003 | 0.000 | 0.000 | 1 | 100 | 0.983 | 0.985 | 0.006 | 0.001 | 7.356 | 0.989 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.527 | 4.055 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.723 | 0.000 | 0.022 | 0.000 | -1 | 100 | NaN | NaN | 0.417 | 0.000 | 1.734 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.038 | 0.002 | 0.000 | 0.000 | -1 | 100 | 0.983 | 0.975 | 0.001 | 0.000 | 54.783 | 15.504 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 20.947 | 15.827 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.725 | 0.000 | 0.022 | 0.000 | 1 | 5 | NaN | NaN | 0.423 | 0.000 | 1.713 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.023 | 0.001 | 0.001 | 0.000 | 1 | 5 | 0.979 | 0.985 | 0.007 | 0.001 | 3.490 | 0.563 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.026 | 3.388 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.600 | 0.0 | 0.800 | 0.000 | k-means++ | NaN | 30 | NaN | 0.363 | 0.0 | 1.653 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.382 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 9.402 | 6.605 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 11.181 | 7.798 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.474 | 0.0 | 1.012 | 0.000 | random | NaN | 30 | NaN | 0.413 | 0.0 | 1.147 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.383 | 0.000 | random | 0.001 | 30 | 0.000 | 0.000 | 0.0 | 9.478 | 6.187 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.036 | 6.698 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 5.801 | 0.0 | 4.137 | 0.000 | k-means++ | NaN | 30 | NaN | 2.556 | 0.0 | 2.270 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.948 | 0.000 | k-means++ | 0.002 | 30 | 0.001 | 0.000 | 0.0 | 5.890 | 2.602 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.863 | 5.735 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 5.314 | 0.0 | 4.516 | 0.000 | random | NaN | 30 | NaN | 2.706 | 0.0 | 1.964 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.001 | 0.0 | 16.036 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.838 | 2.620 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.794 | 5.509 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.073 | 0.0 | 0.044 | 0.000 | random | NaN | 20 | NaN | 0.084 | 0.0 | 0.865 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.195 | 0.000 | random | 0.001 | 20 | -0.002 | 0.001 | 0.0 | 2.768 | 0.453 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.812 | 5.560 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.214 | 0.0 | 0.015 | 0.000 | k-means++ | NaN | 20 | NaN | 0.029 | 0.0 | 7.354 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.194 | 0.000 | k-means++ | -0.001 | 20 | -0.001 | 0.001 | 0.0 | 2.688 | 0.558 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.600 | 5.506 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.186 | 0.0 | 0.860 | 0.000 | random | NaN | 20 | NaN | 0.322 | 0.0 | 0.578 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.914 | 0.000 | random | 0.300 | 20 | 0.319 | 0.001 | 0.0 | 2.154 | 0.332 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.287 | 4.194 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.552 | 0.0 | 0.290 | 0.000 | k-means++ | NaN | 20 | NaN | 0.124 | 0.0 | 4.443 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.807 | 0.000 | k-means++ | 0.266 | 20 | 0.324 | 0.001 | 0.0 | 2.249 | 0.350 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.103 | 4.036 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 9.969 | 0.0 | [-0.11835882] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.710 | 0.0 | 5.831 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [59.62033771] | 0.000 | NaN | NaN | NaN | NaN | 0.567 | 0.000 | 0.0 | 0.818 | 0.377 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.24690939] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.398 | 0.386 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 0.725 | 0.0 | [2.86939723] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.634 | 0.0 | 1.144 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.002 | 0.0 | [137.0560299] | 0.000 | NaN | NaN | NaN | NaN | 0.250 | 0.003 | 0.0 | 0.548 | 0.096 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [25.25451821] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.001 | 0.0 | 0.123 | 0.090 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.168 | 0.0 | 0.477 | 0.0 | NaN | NaN | NaN | 0.171 | 0.0 | 0.983 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.010 | 0.0 | 8.322 | 0.0 | NaN | NaN | 0.1 | 0.016 | 0.0 | 0.595 | 0.028 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 1.246 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.648 | 0.606 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.279 | 0.0 | 0.625 | 0.0 | NaN | NaN | NaN | 0.221 | 0.0 | 5.777 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 5.122 | 0.0 | NaN | NaN | 1.0 | 0.000 | 0.0 | 0.713 | 0.491 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.013 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.688 | 0.657 | See | See |
reporting_hpo = ReportingHpo(files=[
"results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
"results/benchmarking/xgboost_XGBClassifier.csv",
"results/benchmarking/lightgbm_LGBMClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier_symmetric.csv",
])
reporting_hpo.run()